MAM: Mixed Attention Module with Random Disruption Augmentation for Image Classification

Visual attention mechanism extracts features by establishing a mask branch characterizing the distribution regions we are interested in. With the thought that eliminating useless features helps in the process of extracting significant features, in this paper we design a novel visual attention mechanism structure called the mixed attention module (MAM), which is capable of distilling significant patterns as well as suppressing unimportant features. The generic MAM constructed by the min-pooling and the tanh function is embedded into a mixed attention network for image classification. Moreover, a new image enhancement method named the random disruption (RandomDisrupt) is developed by slicing an image and then scrambling it. RandomDisrupt destroys the positional information between the features and the structural information of the object to accomplish data augmentation. The combination of the MAM and RandomDisrupt has improved the image classification performance significantly. Benchmark experiments are conducted to show the superiority of the proposed algorithm over a state-of-the-art method. Source code would be available at https://github.com/weiyu-zeng/MAM.